3 citations · 5 across the 2 of their papers we have counts for
5 papers
Adaptive Dialog Policy Learning with Hindsight and User Modeling
Yan Cao, Keting Lu, Xiaoping Chen +1
Reinforcement learning methods have been used to compute dialog policies from language-based interaction experiences. Efficiency is of particular importance in dialog policy learni…
Learning and Reasoning for Robot Dialog and Navigation Tasks
Keting Lu, Shiqi Zhang, Peter Stone +1
Reinforcement learning and probabilistic reasoning algorithms aim at learning from interaction experiences and reasoning with probabilistic contextual knowledge respectively. In th…
AutoEG: Automated Experience Grafting for Off-Policy Deep Reinforcement Learning
Keting Lu, Shiqi Zhang, Xiaoping Chen
Deep reinforcement learning (RL) algorithms frequently require prohibitive interaction experience to ensure the quality of learned policies. The limitation is partly because the ag…
Robot Representation and Reasoning with Knowledge from Reinforcement Learning
Keting Lu, Shiqi Zhang, Peter Stone +1
Reinforcement learning (RL) agents aim at learning by interacting with an environment, and are not designed for representing or reasoning with declarative knowledge. Knowledge repr…
Goal-oriented Dialogue Policy Learning from Failures
Keting Lu, Shiqi Zhang, Xiaoping Chen
Reinforcement learning methods have been used for learning dialogue policies. However, learning an effective dialogue policy frequently requires prohibitively many conversations. T…